Analysis of GLDS-208 from NASA GeneLab
This R markdown file was auto-generated by the iDEP website Using iDEP 0.91, originally by Steven Xijin.Ge@sdstate.edu
Ge SX, Son EW, Yao R: iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinformatics 2018, 19(1):534. PMID:30567491
First we set up the working directory to where the files are saved.
setwd('~/Documents/HTML_R/GLDS208_RNASEQ')
R packages and iDEP core Functions. Users can also download the iDEP_core_functions.R file. Many R packages needs to be installed first. This may take hours. Each of these packages took years to develop.So be a patient thief. Sometimes dependencies needs to be installed manually. If you are using an older version of R, and having trouble with package installation, try un-install the current version of R, delete all folders and files (C:/Program Files/R/R-3.4.3), and reinstall from scratch.
if(file.exists('iDEP_core_functions.R'))
source('iDEP_core_functions.R') else
source('https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/iDEP_core_functions.R')
We are using the downloaded gene expression file where gene IDs has been converted to Ensembl gene IDs. This is because the ID conversion database is too large to download. You can use your original file if your file uses Ensembl ID, or you do not want to use the pathway files available in iDEP (or it is not available).
inputFile <- 'GLDS208_Expression.csv'
sampleInfoFile <- 'GLDS208_Sampleinfo.csv'
gldsMetadataFile <- 'GLDS208_Metadata.csv'
geneInfoFile <- 'Arabidopsis_thaliana__athaliana_eg_gene_GeneInfo.csv' #Gene symbols, location etc.
geneSetFile <- 'Arabidopsis_thaliana__athaliana_eg_gene.db' # pathway database in SQL; can be GMT format
STRING10_speciesFile <- 'https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/STRING10_species.csv'
Parameters for reading data
input_missingValue <- 'geneMedian' #Missing values imputation method
input_dataFileFormat <- 1 #1- read counts, 2 FKPM/RPKM or DNA microarray
input_minCounts <- 0.5 #Min counts
input_NminSamples <- 1 #Minimum number of samples
input_countsLogStart <- 4 #Pseudo count for log CPM
input_CountsTransform <- 1 #Methods for data transformation of counts. 1-EdgeR's logCPM 2-VST, 3-rlog
readMetadata.out <- readMetadata(gldsMetadataFile)
library(knitr) # install if needed. for showing tables with kable
library(kableExtra)
kable( readMetadata.out ) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Atha_Col0_Root_ZoneI_Rep1 | Atha_Col0_Root_ZoneI_Rep2 | Atha_Col0_Root_ZoneI_Rep3 | Atha_Col0_Root_ZoneII_Rep1 | Atha_Col0_Root_ZoneII_Rep2 | Atha_Col0_Root_ZoneII_Rep3 | |
|---|---|---|---|---|---|---|
| Sample.LongId | Atha.Col.0.Root.Zone.I.Rep1.RNAseq.RNAseq | Atha.Col.0.Root.Zone.I.Rep2.RNAseq.RNAseq | Atha.Col.0.Root.Zone.I.Rep3.RNAseq.RNAseq | Atha.Col.0.Root.Zone.II.Rep1.RNAseq.RNAseq | Atha.Col.0.Root.Zone.II.Rep2.RNAseq.RNAseq | Atha.Col.0.Root.Zone.II.Rep3.RNAseq.RNAseq |
| Sample.Id | Atha.Col.0.Root.Zone.I.Rep1 | Atha.Col.0.Root.Zone.I.Rep2 | Atha.Col.0.Root.Zone.I.Rep3 | Atha.Col.0.Root.Zone.II.Rep1 | Atha.Col.0.Root.Zone.II.Rep2 | Atha.Col.0.Root.Zone.II.Rep3 |
| Sample.Name | Atha_Col-0_Root_Zone-I_Rep1 | Atha_Col-0_Root_Zone-I_Rep2 | Atha_Col-0_Root_Zone-I_Rep3 | Atha_Col-0_Root_Zone-II_Rep1 | Atha_Col-0_Root_Zone-II_Rep2 | Atha_Col-0_Root_Zone-II_Rep3 |
| GLDS | 208 | 208 | 208 | 208 | 208 | 208 |
| Accession | GLDS-208 | GLDS-208 | GLDS-208 | GLDS-208 | GLDS-208 | GLDS-208 |
| Hardware | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish |
| Tissue | Meristem zone 1 | Meristem zone 1 | Meristem zone 1 | Elongation zone 2 | Elongation zone 2 | Elongation zone 2 |
| Age | 8 days | 8 days | 8 days | 8 days | 8 days | 8 days |
| Organism | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana |
| Ecotype | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 |
| Genotype | WT | WT | WT | WT | WT | WT |
| Variety | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT |
| Radiation | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth |
| Gravity | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial |
| Developmental | 8 day old seedlings root tip meristem and elongation zone | 8 day old seedlings root tip meristem and elongation zone | 8 day old seedlings root tip meristem and elongation zone | 8 day old seedlings root tip meristem and elongation zone | 8 day old seedlings root tip meristem and elongation zone | 8 day old seedlings root tip meristem and elongation zone |
| Time.series.or.Concentration.gradient | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point |
| Light | White light | White light | White light | White light | White light | White light |
| Assay..RNAseq. | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling |
| Temperature | 18-20 | 18-20 | 18-20 | 18-20 | 18-20 | 18-20 |
| Treatment.type | Comparative gene expression analysis in the Arabidopsis thaliana root apex using RNA-seq and microarray transcriptome profiles (microarray) | Comparative gene expression analysis in the Arabidopsis thaliana root apex using RNA-seq and microarray transcriptome profiles (microarray) | Comparative gene expression analysis in the Arabidopsis thaliana root apex using RNA-seq and microarray transcriptome profiles (microarray) | Comparative gene expression analysis in the Arabidopsis thaliana root apex using RNA-seq and microarray transcriptome profiles (microarray) | Comparative gene expression analysis in the Arabidopsis thaliana root apex using RNA-seq and microarray transcriptome profiles (microarray) | Comparative gene expression analysis in the Arabidopsis thaliana root apex using RNA-seq and microarray transcriptome profiles (microarray) |
| Treatment.intensity | x | x | x | x | x | x |
| Treament.timing | x | x | x | x | x | x |
| Preservation.Method. | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater |
readData.out <- readData(inputFile)
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
kable( head(readData.out$data) ) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Atha_Col0_Root_ZoneI_Rep1 | Atha_Col0_Root_ZoneI_Rep2 | Atha_Col0_Root_ZoneI_Rep3 | Atha_Col0_Root_ZoneII_Rep1 | Atha_Col0_Root_ZoneII_Rep2 | Atha_Col0_Root_ZoneII_Rep3 | |
|---|---|---|---|---|---|---|
| AT1G75750 | 14.88189 | 14.96816 | 14.53326 | 8.50174 | 5.264568 | 2.796958 |
| AT5G56540 | 14.27907 | 15.06916 | 14.87850 | 10.96798 | 10.427013 | 9.505186 |
| AT1G62480 | 14.77619 | 15.18363 | 15.10583 | 13.56028 | 12.948680 | 12.509942 |
| AT4G34050 | 14.81464 | 14.78146 | 14.56046 | 11.06152 | 10.170371 | 10.517753 |
| AT1G21310 | 15.07387 | 14.56679 | 14.81694 | 12.49484 | 12.237116 | 12.634518 |
| AT2G02130 | 14.77509 | 14.74724 | 14.47653 | 11.70054 | 10.777718 | 10.785445 |
readSampleInfo.out <- readSampleInfo(sampleInfoFile)
kable( readSampleInfo.out ) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Tissue | |
|---|---|
| Atha_Col0_Root_ZoneI_Rep1 | Meristem zone 1 |
| Atha_Col0_Root_ZoneI_Rep2 | Meristem zone 1 |
| Atha_Col0_Root_ZoneI_Rep3 | Meristem zone 1 |
| Atha_Col0_Root_ZoneII_Rep1 | Elongation zone 2 |
| Atha_Col0_Root_ZoneII_Rep2 | Elongation zone 2 |
| Atha_Col0_Root_ZoneII_Rep3 | Elongation zone 2 |
input_selectOrg ="NEW"
input_selectGO <- 'GOBP' #Gene set category
input_noIDConversion = TRUE
allGeneInfo.out <- geneInfo(geneInfoFile)
converted.out = NULL
convertedData.out <- convertedData()
nGenesFilter()
## [1] "16156 genes in 6 samples. 16156 genes passed filter.\n Original gene IDs used."
convertedCounts.out <- convertedCounts() # converted counts, just for compatibility
# Read counts per library
parDefault = par()
par(mar=c(12,4,2,2))
# barplot of total read counts
x <- readData.out$rawCounts
groups = as.factor( detectGroups(colnames(x ) ) )
if(nlevels(groups)<=1 | nlevels(groups) >20 )
col1 = 'green' else
col1 = rainbow(nlevels(groups))[ groups ]
barplot( colSums(x)/1e6,
col=col1,las=3, main="Total read counts (millions)")
readCountsBias() # detecting bias in sequencing depth
## [1] 0.01124929
## [1] 0.01124929
## [1] "Warning! Sequencing depth bias detected. Total read counts are significantly different among sample groups (p= 1.12e-02 ) based on ANOVA. Total read counts seem to be correlated with factor Tissue (p= 1.12e-02 ). "
# Box plot
x = readData.out$data
boxplot(x, las = 2, col=col1,
ylab='Transformed expression levels',
main='Distribution of transformed data')
#Density plot
par(parDefault)
## Warning in par(parDefault): graphical parameter "cin" cannot be set
## Warning in par(parDefault): graphical parameter "cra" cannot be set
## Warning in par(parDefault): graphical parameter "csi" cannot be set
## Warning in par(parDefault): graphical parameter "cxy" cannot be set
## Warning in par(parDefault): graphical parameter "din" cannot be set
## Warning in par(parDefault): graphical parameter "page" cannot be set
densityPlot()
# Scatter plot of the first two samples
plot(x[,1:2],xlab=colnames(x)[1],ylab=colnames(x)[2],
main='Scatter plot of first two samples')
####plot gene or gene family
input_selectOrg ="BestMatch"
input_geneSearch <- 'HOXA' #Gene ID for searching
genePlot()
## NULL
input_useSD <- 'FALSE' #Use standard deviation instead of standard error in error bar?
geneBarPlotError()
## NULL
# hierarchical clustering tree
x <- readData.out$data
maxGene <- apply(x,1,max)
# remove bottom 25% lowly expressed genes, which inflate the PPC
x <- x[which(maxGene > quantile(maxGene)[1] ) ,]
plot(as.dendrogram(hclust2( dist2(t(x)))), ylab="1 - Pearson C.C.", type = "rectangle")
#Correlation matrix
input_labelPCC <- TRUE #Show correlation coefficient?
correlationMatrix()
# Parameters for heatmap
input_nGenes <- 1000 #Top genes for heatmap
input_geneCentering <- TRUE #centering genes ?
input_sampleCentering <- FALSE #Center by sample?
input_geneNormalize <- FALSE #Normalize by gene?
input_sampleNormalize <- FALSE #Normalize by sample?
input_noSampleClustering <- FALSE #Use original sample order
input_heatmapCutoff <- 4 #Remove outliers beyond number of SDs
input_distFunctions <- 1 #which distant funciton to use
input_hclustFunctions <- 1 #Linkage type
input_heatColors1 <- 1 #Colors
input_selectFactorsHeatmap <- 'Tissue' #Sample coloring factors
png('heatmap.png', width = 10, height = 15, units = 'in', res = 300)
staticHeatmap()
dev.off()
## png
## 2
[heatmap] (heatmap.png)
heatmapPlotly() # interactive heatmap using Plotly
input_nGenesKNN <- 2000 #Number of genes fro k-Means
input_nClusters <- 4 #Number of clusters
maxGeneClustering = 12000
input_kmeansNormalization <- 'geneMean' #Normalization
input_KmeansReRun <- 0 #Random seed
distributionSD() #Distribution of standard deviations
KmeansNclusters() #Number of clusters
Kmeans.out = Kmeans() #Running K-means
KmeansHeatmap() #Heatmap for k-Means
#Read gene sets for enrichment analysis
sqlite <- dbDriver('SQLite')
input_selectGO3 <- 'GOBP' #Gene set category
input_minSetSize <- 15 #Min gene set size
input_maxSetSize <- 2000 #Max gene set size
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO3,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
# Alternatively, users can use their own GMT files by
#GeneSets.out <- readGMTRobust('somefile.GMT')
results <- KmeansGO() #Enrichment analysis for k-Means clusters
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Cluster | adj.Pval | Genes | Pathways |
|---|---|---|---|
| A | 9.41e-229 | 253 | Amide biosynthetic process |
| 4.69e-228 | 244 | Translation | |
| 1.31e-227 | 244 | Peptide biosynthetic process | |
| 6.81e-221 | 247 | Peptide metabolic process | |
| 1.32e-216 | 258 | Cellular amide metabolic process | |
| 4.79e-180 | 290 | Organonitrogen compound biosynthetic process | |
| 1.36e-129 | 153 | Ribonucleoprotein complex biogenesis | |
| 2.34e-110 | 127 | Ribosome biogenesis | |
| 3.94e-89 | 185 | Cellular component biogenesis | |
| 9.73e-79 | 66 | Ribosomal large subunit biogenesis | |
| B | 7.37e-49 | 128 | Oxidation-reduction process |
| 6.91e-39 | 127 | Small molecule metabolic process | |
| 8.49e-39 | 83 | Drug metabolic process | |
| 1.97e-30 | 62 | Nucleobase-containing small molecule metabolic process | |
| 1.29e-28 | 122 | Response to abiotic stimulus | |
| 4.83e-27 | 77 | Response to inorganic substance | |
| 9.37e-26 | 54 | Generation of precursor metabolites and energy | |
| 2.28e-25 | 54 | Response to metal ion | |
| 3.17e-25 | 34 | ATP metabolic process | |
| 3.17e-25 | 44 | Purine-containing compound metabolic process | |
| C | 2.40e-21 | 36 | Cell wall organization or biogenesis |
| 6.26e-21 | 32 | Cell wall organization | |
| 2.20e-20 | 32 | External encapsulating structure organization | |
| 1.12e-13 | 20 | Plant-type cell wall organization or biogenesis | |
| 2.77e-10 | 15 | Drug catabolic process | |
| 3.26e-10 | 11 | Hydrogen peroxide catabolic process | |
| 7.32e-10 | 12 | Phenylpropanoid metabolic process | |
| 1.97e-09 | 11 | Antibiotic catabolic process | |
| 2.17e-09 | 13 | Plant-type cell wall organization | |
| 2.17e-09 | 15 | Cell wall biogenesis | |
| D | 5.09e-16 | 30 | Secondary metabolic process |
| 2.61e-13 | 18 | Phenylpropanoid metabolic process | |
| 6.94e-13 | 15 | Phenylpropanoid biosynthetic process | |
| 6.94e-13 | 19 | Secondary metabolite biosynthetic process | |
| 1.57e-12 | 14 | Lignin metabolic process | |
| 1.78e-10 | 11 | Lignin biosynthetic process | |
| 2.79e-10 | 53 | Response to oxygen-containing compound | |
| 3.61e-09 | 58 | Response to organic substance | |
| 5.56e-09 | 60 | Response to abiotic stimulus | |
| 5.56e-09 | 37 | Response to inorganic substance |
input_seedTSNE <- 0 #Random seed for t-SNE
input_colorGenes <- TRUE #Color genes in t-SNE plot?
tSNEgenePlot() #Plot genes using t-SNE
input_selectFactors <- 'Tissue' #Factor coded by color
input_selectFactors2 <- 'Sample_Name' #Factor coded by shape
input_tsneSeed2 <- 0 #Random seed for t-SNE
#PCA, MDS and t-SNE plots
PCAplot()
MDSplot()
tSNEplot()
#Read gene sets for pathway analysis using PGSEA on principal components
input_selectGO6 <- 'GOBP'
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO6,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
PCApathway() # Run PGSEA analysis
## Warning: Package 'KEGG.db' is deprecated and will be removed from Bioconductor
## version 3.12
cat( PCA2factor() ) #The correlation between PCs with factors
##
## Correlation between Principal Components (PCs) with factors
## PC1 is correlated with Tissue (p=1.29e-04).
input_CountsDEGMethod <- 3 #DESeq2= 3,limma-voom=2,limma-trend=1
input_limmaPval <- 0.1 #FDR cutoff
input_limmaFC <- 2 #Fold-change cutoff
input_selectModelComprions <- 'Tissue: Meristem zone 1 vs. Elongation zone 2' #Selected comparisons
input_selectFactorsModel <- 'Tissue' #Selected comparisons
input_selectInteractions <- NULL #Selected comparisons
input_selectBlockFactorsModel <- NULL #Selected comparisons
factorReferenceLevels.out <- c('Tissue:Meristem zone 1')
limma.out <- limma()
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
DEG.data.out <- DEG.data()
limma.out$comparisons
## [1] "Meristem zone 1-Elongation zone 2"
input_selectComparisonsVenn = limma.out$comparisons[1:3] # use first three comparisons
input_UpDownRegulated <- FALSE #Split up and down regulated genes
vennPlot() # Venn diagram
sigGeneStats() # number of DEGs as figure
sigGeneStatsTable() # number of DEGs as table
## Comparisons Up Down
## Meristem-zone-1-Elongation-zone-2 Meristem-zone-1-Elongation-zone-2 4621 3642
input_selectContrast <- 'Meristem zone 1-Elongation zone 2' #Selected comparisons
selectedHeatmap.data.out <- selectedHeatmap.data()
selectedHeatmap() # heatmap for DEGs in selected comparison
## Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), : 'data' must be of a vector type, was 'NULL'
# Save gene lists and data into files
write.csv( selectedHeatmap.data()$genes, 'heatmap.data.csv')
write.csv(DEG.data(),'DEG.data.csv' )
write(AllGeneListsGMT() ,'AllGeneListsGMT.gmt')
input_selectGO2 <- 'GOBP' #Gene set category
geneListData.out <- geneListData()
volcanoPlot()
scatterPlot()
MAplot()
geneListGOTable.out <- geneListGOTable()
# Read pathway data again
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO2,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
input_removeRedudantSets <- TRUE #Remove highly redundant gene sets?
results <- geneListGO() #Enrichment analysis
## Error in if (dim(results1)[2] == 1) return(results1) else {: argument is of length zero
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Cluster | adj.Pval | Genes | Pathways |
|---|---|---|---|
| A | 9.41e-229 | 253 | Amide biosynthetic process |
| 4.69e-228 | 244 | Translation | |
| 1.31e-227 | 244 | Peptide biosynthetic process | |
| 6.81e-221 | 247 | Peptide metabolic process | |
| 1.32e-216 | 258 | Cellular amide metabolic process | |
| 4.79e-180 | 290 | Organonitrogen compound biosynthetic process | |
| 1.36e-129 | 153 | Ribonucleoprotein complex biogenesis | |
| 2.34e-110 | 127 | Ribosome biogenesis | |
| 3.94e-89 | 185 | Cellular component biogenesis | |
| 9.73e-79 | 66 | Ribosomal large subunit biogenesis | |
| B | 7.37e-49 | 128 | Oxidation-reduction process |
| 6.91e-39 | 127 | Small molecule metabolic process | |
| 8.49e-39 | 83 | Drug metabolic process | |
| 1.97e-30 | 62 | Nucleobase-containing small molecule metabolic process | |
| 1.29e-28 | 122 | Response to abiotic stimulus | |
| 4.83e-27 | 77 | Response to inorganic substance | |
| 9.37e-26 | 54 | Generation of precursor metabolites and energy | |
| 2.28e-25 | 54 | Response to metal ion | |
| 3.17e-25 | 34 | ATP metabolic process | |
| 3.17e-25 | 44 | Purine-containing compound metabolic process | |
| C | 2.40e-21 | 36 | Cell wall organization or biogenesis |
| 6.26e-21 | 32 | Cell wall organization | |
| 2.20e-20 | 32 | External encapsulating structure organization | |
| 1.12e-13 | 20 | Plant-type cell wall organization or biogenesis | |
| 2.77e-10 | 15 | Drug catabolic process | |
| 3.26e-10 | 11 | Hydrogen peroxide catabolic process | |
| 7.32e-10 | 12 | Phenylpropanoid metabolic process | |
| 1.97e-09 | 11 | Antibiotic catabolic process | |
| 2.17e-09 | 13 | Plant-type cell wall organization | |
| 2.17e-09 | 15 | Cell wall biogenesis | |
| D | 5.09e-16 | 30 | Secondary metabolic process |
| 2.61e-13 | 18 | Phenylpropanoid metabolic process | |
| 6.94e-13 | 15 | Phenylpropanoid biosynthetic process | |
| 6.94e-13 | 19 | Secondary metabolite biosynthetic process | |
| 1.57e-12 | 14 | Lignin metabolic process | |
| 1.78e-10 | 11 | Lignin biosynthetic process | |
| 2.79e-10 | 53 | Response to oxygen-containing compound | |
| 3.61e-09 | 58 | Response to organic substance | |
| 5.56e-09 | 60 | Response to abiotic stimulus | |
| 5.56e-09 | 37 | Response to inorganic substance |
STRING-db API access. We need to find the taxonomy id of your species, this used by STRING. First we try to guess the ID based on iDEP’s database. Users can also skip this step and assign NCBI taxonomy id directly by findTaxonomyID.out = 10090 # mouse 10090, human 9606 etc.
STRING10_species = read.csv(STRING10_speciesFile)
ix = grep('Arabidopsis thaliana', STRING10_species$official_name )
findTaxonomyID.out <- STRING10_species[ix,1] # find taxonomyID
findTaxonomyID.out
## [1] 3702
Enrichment analysis using STRING
STRINGdb_geneList.out <- STRINGdb_geneList() #convert gene lists
## Warning: we couldn't map to STRING 0% of your identifiers
input_STRINGdbGO <- 'Process' #'Process', 'Component', 'Function', 'KEGG', 'Pfam', 'InterPro'
results <- stringDB_GO_enrichmentData() # enrichment using STRING
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
|
PPI network retrieval and analysis
input_nGenesPPI <- 100 #Number of top genes for PPI retrieval and analysis
stringDB_network1(1) #Show PPI network
Generating interactive PPI
write(stringDB_network_link(), 'PPI_results.html') # write results to html file
## Warning: 'string_db$get_link' is deprecated.
## Use 'Contact developers to request functionality' instead.
## See help("Deprecated")
## Warning: we couldn't map to STRING 0% of your identifiers
## Warning: 'string_db$get_link' is deprecated.
## Use 'Contact developers to request functionality' instead.
## See help("Deprecated")
## Warning: 'string_db$get_link' is deprecated.
## Use 'Contact developers to request functionality' instead.
## See help("Deprecated")
browseURL('PPI_results.html') # open in browser
input_selectContrast1 <- 'Meristem zone 1-Elongation zone 2' #select Comparison
#input_selectContrast1 = limma.out$comparisons[3] # manually set
input_selectGO <- 'GOBP' #Gene set category
#input_selectGO='custom' # if custom gmt file
input_minSetSize <- 15 #Min size for gene set
input_maxSetSize <- 2000 #Max size for gene set
# Read pathway data again
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
input_pathwayPvalCutoff <- 0.2 #FDR cutoff
input_nPathwayShow <- 30 #Top pathways to show
input_absoluteFold <- FALSE #Use absolute values of fold-change?
input_GenePvalCutoff <- 1 #FDR to remove genes
input_pathwayMethod = 1 # 1 GAGE
gagePathwayData.out <- gagePathwayData() # pathway analysis using GAGE
results <- gagePathwayData.out #Enrichment analysis for k-Means clusters
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Direction | GAGE analysis: Meristem zone 1 vs Elongation zone 2 | statistic | Genes | adj.Pval |
|---|---|---|---|---|
| Down | Ribonucleoprotein complex biogenesis | -14.442 | 438 | 1.9e-37 |
| Ribosome biogenesis | -13.9246 | 343 | 5.8e-34 | |
| RNA modification | -12.523 | 321 | 1.8e-29 | |
| NcRNA metabolic process | -12.5069 | 425 | 1.0e-29 | |
| NcRNA processing | -12.0699 | 357 | 1.6e-27 | |
| RRNA processing | -11.3138 | 239 | 8.3e-23 | |
| RRNA metabolic process | -11.215 | 244 | 1.2e-22 | |
| Ribosomal large subunit biogenesis | -8.6084 | 99 | 4.2e-12 | |
| MRNA processing | -8.53 | 378 | 1.9e-14 | |
| Chromatin organization | -8.4884 | 384 | 1.9e-14 | |
| Ribonucleoprotein complex assembly | -8.3357 | 174 | 7.0e-13 | |
| Macromolecule methylation | -8.3169 | 211 | 2.8e-13 | |
| Ribonucleoprotein complex subunit organization | -8.2828 | 182 | 7.5e-13 | |
| MRNA metabolic process | -8.2476 | 481 | 6.8e-14 | |
| Cell cycle process | -7.687 | 396 | 3.3e-12 | |
| RNA splicing | -7.5065 | 265 | 3.4e-11 | |
| Ribosomal small subunit biogenesis | -7.3335 | 80 | 3.9e-09 | |
| Ribosome assembly | -7.2635 | 77 | 7.4e-09 | |
| DNA repair | -7.2529 | 314 | 9.0e-11 | |
| Cellular response to DNA damage stimulus | -7.2318 | 337 | 9.0e-11 | |
| Organelle fission | -6.8164 | 226 | 1.8e-09 | |
| Nuclear division | -6.7864 | 189 | 2.5e-09 | |
| TRNA metabolic process | -6.5216 | 170 | 1.3e-08 | |
| RNA splicing, via transesterification reactions | -6.4989 | 194 | 1.7e-08 | |
| RNA splicing, via transesterification reactions with bulged adenosine as nucleophile | -6.4989 | 194 | 1.7e-08 | |
| DNA conformation change | -6.2018 | 142 | 8.7e-08 | |
| Mitotic cell cycle process | -6.1421 | 235 | 6.5e-08 | |
| Organelle assembly | -6.1273 | 154 | 1.3e-07 | |
| DNA recombination | -6.126 | 151 | 1.3e-07 | |
| Methylation | -5.9282 | 364 | 1.5e-07 |
pathwayListData.out = pathwayListData()
enrichmentPlot(pathwayListData.out, 25 )
enrichmentNetwork(pathwayListData.out )
enrichmentNetworkPlotly(pathwayListData.out)
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
input_pathwayMethod = 3 # 1 fgsea
fgseaPathwayData.out <- fgseaPathwayData() #Pathway analysis using fgsea
## Warning in fgsea(pathways = gmt, stats = fold, minSize = input_minSetSize, :
## You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To
## run fgseaMultilevel, you need to remove the nperm argument in the fgsea function
## call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.06% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgseaSimple(...): There were 8 pathways for which P-values were not
## calculated properly due to unbalanced gene-level statistic values
results <- fgseaPathwayData.out #Enrichment analysis for k-Means clusters
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Direction | GSEA analysis: Meristem zone 1 vs Elongation zone 2 | NES | Genes | adj.Pval |
|---|---|---|---|---|
| Down | Ribosome biogenesis | -3.506 | 343 | 6.3e-02 |
| Ribonucleoprotein complex biogenesis | -3.3891 | 438 | 1.7e-01 | |
| RRNA processing | -3.3385 | 239 | 2.6e-02 | |
| Ribosomal large subunit biogenesis | -3.3217 | 99 | 6.9e-03 | |
| RRNA metabolic process | -3.303 | 244 | 2.8e-02 | |
| RNA modification | -3.2379 | 321 | 5.5e-02 | |
| Ribosome assembly | -3.1058 | 77 | 5.9e-03 | |
| NcRNA processing | -3.0606 | 357 | 7.5e-02 | |
| Ribosomal small subunit biogenesis | -3.0603 | 80 | 6.0e-03 | |
| NcRNA metabolic process | -3.0486 | 425 | 1.8e-01 | |
| Ribonucleoprotein complex assembly | -2.9182 | 174 | 1.4e-02 | |
| Maturation of LSU-rRNA | -2.8756 | 42 | 5.1e-03 | |
| Ribonucleoprotein complex subunit organization | -2.8685 | 182 | 1.5e-02 | |
| Ribosomal large subunit assembly | -2.8613 | 40 | 4.9e-03 | |
| Maturation of SSU-rRNA | -2.8432 | 52 | 5.1e-03 | |
| Macromolecule methylation | -2.7982 | 211 | 1.9e-02 | |
| RNA methylation | -2.6617 | 64 | 5.4e-03 | |
| Maturation of SSU-rRNA from tricistronic rRNA transcript (SSU-rRNA, 5.8S rRNA, LSU-rRNA) | -2.6365 | 36 | 4.9e-03 | |
| Ribosomal small subunit assembly | -2.6281 | 31 | 4.8e-03 | |
| Organelle assembly | -2.6238 | 154 | 1.1e-02 | |
| Maturation of 5.8S rRNA | -2.6198 | 32 | 4.8e-03 | |
| Mitotic cell cycle phase transition | -2.5546 | 76 | 5.8e-03 | |
| Regulation of mitotic cell cycle phase transition | -2.5513 | 48 | 5.1e-03 | |
| Cell cycle phase transition | -2.5336 | 81 | 6.1e-03 | |
| Mitochondrial RNA metabolic process | -2.5208 | 55 | 5.1e-03 | |
| Meiosis I cell cycle process | -2.5124 | 62 | 5.4e-03 | |
| Cleavage involved in rRNA processing | -2.5124 | 24 | 4.6e-03 | |
| Maturation of 5.8S rRNA from tricistronic rRNA transcript (SSU-rRNA, 5.8S rRNA, LSU-rRNA) | -2.5016 | 23 | 4.6e-03 | |
| MRNA modification | -2.4782 | 43 | 5.1e-03 | |
| Regulation of cell cycle phase transition | -2.4729 | 52 | 5.1e-03 |
pathwayListData.out = pathwayListData()
enrichmentPlot(pathwayListData.out, 25 )
enrichmentNetwork(pathwayListData.out )
enrichmentNetworkPlotly(pathwayListData.out)
PGSEAplot() # pathway analysis using PGSEA
##
## Computing P values using ANOVA
input_selectContrast2 <- 'Meristem zone 1-Elongation zone 2' #select Comparison
#input_selectContrast2 = limma.out$comparisons[3] # manually set
input_limmaPvalViz <- 0.1 #FDR to filter genes
input_limmaFCViz <- 2 #FDR to filter genes
genomePlotly() # shows fold-changes on the genome
## Warning in eval(quote(list(...)), env): NAs introduced by coercion
## Warning in genomePlotly(): NAs introduced by coercion
input_nGenesBiclust <- 1000 #Top genes for biclustering
input_biclustMethod <- 'BCCC()' #Method: 'BCCC', 'QUBIC', 'runibic' ...
biclustering.out = biclustering() # run analysis
input_selectBicluster <- 1 #select a cluster
biclustHeatmap() # heatmap for selected cluster
input_selectGO4 <- 'GOBP' #Gene set category
# Read pathway data again
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO4,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
results <- geneListBclustGO() #Enrichment analysis for k-Means clusters
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| adj.Pval | Genes | Pathways |
|---|---|---|
| 7.8e-131 | 174 | Peptide metabolic process |
| 4.6e-128 | 166 | Peptide biosynthetic process |
| 4.6e-128 | 182 | Cellular amide metabolic process |
| 2.5e-127 | 165 | Translation |
| 2.5e-127 | 171 | Amide biosynthetic process |
| 7.7e-106 | 208 | Organonitrogen compound biosynthetic process |
| 6.7e-48 | 70 | Response to cadmium ion |
| 7.1e-44 | 75 | Response to metal ion |
| 6.2e-43 | 77 | Ribonucleoprotein complex biogenesis |
| 1.8e-41 | 98 | Response to inorganic substance |
input_mySoftPower <- 5 #SoftPower to cutoff
input_nGenesNetwork <- 1000 #Number of top genes
input_minModuleSize <- 20 #Module size minimum
wgcna.out = wgcna() # run WGCNA
## Warning: executing %dopar% sequentially: no parallel backend registered
## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.381 1.890 0.903 904 936 949
## 2 2 0.316 1.080 0.864 845 893 916
## 3 3 0.302 0.910 0.885 799 856 890
## 4 4 0.288 0.763 0.877 761 825 867
## 5 5 0.241 0.628 0.814 728 796 846
## 6 6 0.291 0.722 0.851 698 770 827
## 7 7 0.223 0.589 0.846 672 745 810
## 8 8 0.242 0.569 0.885 648 723 794
## 9 9 0.211 0.489 0.865 625 701 778
## 10 10 0.207 0.448 0.827 605 681 764
## 11 12 0.220 0.477 0.928 569 644 737
## 12 14 0.210 0.451 0.908 537 611 713
## 13 16 0.231 0.430 0.966 508 582 691
## 14 18 0.228 0.429 0.956 483 553 671
## 15 20 0.204 0.405 0.924 460 528 653
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
softPower() # soft power curve
modulePlot() # plot modules
listWGCNA.Modules.out = listWGCNA.Modules() #modules
input_selectGO5 <- 'GOBP' #Gene set category
# Read pathway data again
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO5,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
input_selectWGCNA.Module <- 'Entire network' #Select a module
input_topGenesNetwork <- 10 #SoftPower to cutoff
input_edgeThreshold <- 0.4 #Number of top genes
moduleNetwork() # show network of top genes in selected module
## softConnectivity: FYI: connecitivty of genes with less than 4 valid samples will be returned as NA.
## ..calculating connectivities..
input_removeRedudantSets <- TRUE #Remove redundant gene sets
results <- networkModuleGO() #Enrichment analysis of selected module
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| adj.Pval | Genes | Pathways |
|---|---|---|
| 5.8e-138 | 207 | Peptide metabolic process |
| 7.9e-135 | 197 | Peptide biosynthetic process |
| 3.0e-134 | 196 | Translation |
| 2.2e-133 | 203 | Amide biosynthetic process |
| 2.8e-133 | 216 | Cellular amide metabolic process |
| 9.5e-104 | 247 | Organonitrogen compound biosynthetic process |
| 5.2e-54 | 92 | Ribosome biogenesis |
| 9.0e-54 | 102 | Ribonucleoprotein complex biogenesis |
| 4.8e-50 | 160 | Cellular component biogenesis |
| 2.3e-48 | 127 | Response to inorganic substance |